Background

Background

Motivated by ecological communities to develop tools for multi-dimensional time series

Time series of rodent populations

Time series of rodent populations

Background

Motivated by ecological communities to develop tools for multi-dimensional time series

Time series of rodent populations with putative qualitative changes noted

Time series of rodent populations with putative qualitative changes noted

Background

Quantify how ecosystems are responding to stressors

  • Climate change
  • Invasive species
  • Landscape alteration

Phase plane of precipitation and temperature at Portal over time

Phase plane of precipitation and temperature at Portal over time

Background

Need to distinguish

  • Stochasticity
  • Autocorrelation
  • Cyclical dynamics
  • Gradual change
  • Abrupt shifts

Time series of rodent populations zoomed in to moons 200 to 400

Time series of rodent populations zoomed in to moons 200 to 400

Background

Goals for LDATS Package

Statistical Approach

Two-stage analyses:

  1. Reduce dimensions
  2. Analyze time series from [1]

Dimension Reduction

Latent Dirichlet Allocation

\(M\) total documents
\(N\) total words
\(w\) word identity
\(z\) topic identity
\(\theta\) topics-in-documents
\(\alpha\) Dirichlet parameter for \(\theta\)
\(\beta\) terms-in-topics

Plate notation for Latent Dirichlet Allocation

Plate notation for Latent Dirichlet Allocation

   
Bookshelf

Bookshelf

Dimension Reduction

Latent Dirichlet Allocation

Matrix decomposition representation of LDA

Matrix decomposition representation of LDA

 
Bookshelf

Bookshelf

Dimension Reduction

Dimension Reduction

Time Series

Abrupt change points

Fit using parallel tempering MCMC

ptMCMC

LDATS Package

Top-level API target: an “lm-style”

LDA_TS(data, topics = 2, nseeds = 1, formulas = ~1, nchangepoints = 0, timename = "time", weights = TRUE, control = list())

LDATS Package

Under-the-hood

LDATS Package

LDATS v0.2.7

Released on CRAN

LDATS Package

LDATS v0.3.0

In development on GitHub

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A caption

A caption

Acknowledgements

Funding

National Science Foundation: DEB-1622425, DGE-1315138, DGE-1842473

Gordon and Betty Moore Foundation: Data-Driven Discovery Initiative Grant GBMF4563